Early bottom-up attention models [1]}, [2]} mainly adopted hand-designing features (e.g., intensity,
color, and edge orientation) or heuristics (e.g., center-surround contrast [2]}) based on limited human knowledge on visual
attention. Recently, it has observed a new wave of development [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]} using Convolutional Neural Networks (CNNs) that emphasizes the importance of automatic hierarchical feature extraction and end-to-end task learning. Provided with
enough training data, deep learning architectures have been shown impressive performance on a
diverse set of visual tasks, ranging from a global scale image classification
[11]}, to a more local object detection [12]} or semantic segmentation [13]}.
